Find research datasets worth reusing

(Dataset) Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America

Dataset for the paper: Evaluating tomotectonic plate reconstructions using geodynamic models with data assimilation, the case for North America For more infomation, please look into the README file or contact ljliu@illinois.edu, thank you!

opencc-zeroFeb 2023View details →

Data from: Hidden variable models reveal the effects of infection from changes in host survival

The impacts of disease on host vital rates can be demonstrated using longitudinal studies, but these studies can be expensive and logistically challenging. We examined the utility of hidden variable models to infer the individual effects of infectious disease from population-level measurements of survival when longitudinal studies are not possible. Our approach seeks to explain temporal deviations in population-level survival after introducing a disease causative agent when disease prevalence cannot be directly measured by coupling survival and epidemiological models. We tested this approach using an experimental host system (Drosophila melanogaster) with multiple distinct pathogens to validate the ability of the hidden variable model to infer per-capita disease rates. We then applied the approach to a disease outbreak in harbor seals (Phoca vituline) that had data on observed strandings but no epidemiological data. We found that our hidden variable modeling approach could successfully detect the per-capita effects of disease from monitored survival rates in both the experimental and wild populations. Our approach may prove useful for detecting epidemics from public health data in regions where standard surveillance techniques are not available and in the study of epidemics in wildlife populations, where longitudinal studies can be especially difficult to implement.

opencc-zeroFeb 2023View details →

Prior choice and data requirements of Bayesian multivariate mixed effects models fit to tag-recovery data: The need for power analyses

1. Recent empirical studies have quantified correlation between survival and recovery by estimating these parameters as correlated random effects with hierarchical Bayesian multivariate models fit to tag-recovery data. In these applications, increasingly negative correlation between survival and recovery has been interpreted as evidence for increasingly additive harvest mortality. The power of these hierarchal models to detect non-zero correlations has rarely been evaluated and these few studies have not focused on tag-recovery data, which is a common data type. 2. We assessed the power of multivariate hierarchical models to detect negative correlation between annual survival and recovery. Using three priors for multivariate normal distributions, we fit hierarchical effects models to a mallard (Anas platyrhychos) tag-recovery dataset and to simulated data with sample sizes corresponding to different levels of monitoring intensity. We also demonstrate more robust summary statistics for tag-recovery datasets than total individuals tagged. 3. Different priors lead to substantially different estimates of correlation from the mallard data. Our power analysis of simulated data indicated most prior distribution and sample size combinations could not estimate strongly negative correlation with useful precision or accuracy. Many correlation estimates spanned the available parameter space (–1,1) and underestimated the magnitude of negative correlation. Only one prior combined with our most intensive monitoring scenario provided reliable results. Underestimating the magnitude of correlation coincided with overestimating the variability of annual survival, but not annual recovery. 4. The inadequacy of prior distributions and sample size combinations previously assumed adequate for obtaining robust inference from tag-recovery data represents a concern in the application of Bayesian hierarchical models to tag-recovery data. Our analysis approach provides a means for examining prior influence and sample size on hierarchical models fit to capture-recapture data while emphasizing transferability of results between empirical and simulation studies.

The Mixed Layer Depth in the Ocean Model Intercomparison Project (OMIP): Impact of Resolving Mesoscale Eddies: supporting data

This file contains a jupyter notebook (python language) used to produce the figures of a manuscript submitted to the journal Geoscientific Model Development, and the data necessary to reproduce the figures. Abstract of the manuscript: The ocean mixed layer is the interface between the ocean interior and the atmosphere or sea ice, and plays a key role in climate variability. It is thus critical that numerical models used in climate studies are capable of a good representation of the mixed layer, especially its depth. Here we evaluate the mixed layer depth (MLD) in six pairs of non-eddying (1° resolution) and eddy-rich (up to 1/16°) models from the Ocean Model Intercomparison Project (OMIP), forced by a common atmospheric state. For model validation, we use an updated MLD dataset computed from observations using the OMIP protocol (a constant density threshold). In winter, low resolution models exhibit large biases in the deep water formation regions. These biases are reduced in eddy-rich models but not uniformly across models and regions. The improvement is most noticeable in the mode water formation regions of the northern hemisphere. Results in the Southern Ocean are more contrasted, with biases of either sign remaining at high resolution. In eddy-rich models, mesoscale eddies control the spatial variability of MLD in winter. Contrary to a hypothesis that the deepening of the mixed layer in anticyclones would make the MLD larger globally, eddy-rich models tend to have a shallower mixed layer at most latitudes than coarser models do. In addition, our study highlights the sensitivity of the MLD computation to the choice of a reference level and the spatio-temporal sampling, which motivates new recommendations for MLD computation in future model intercomparison projects.

name lists for 'Detecting intersectionality in NER models: A data-driven approach'

Name lists used for data augmentation for testing biases (in terms of error disparities) of Name Entity Recognition in Danish NLP pipelines. The following lists are from <a href="https://www.dst.dk/da/Statistik/emner/borgere/navne/navne-i-hele-befolkningen">Statistics Denmark</a>: <ul> <li>majority_first_names_2023_men.csv</li> <li>majority_first_names_2023_women.csv</li> <li>majority_last_names_2023.csv</li> </ul> The following lists are from Eva Villarsen Meldgaard. 2005. <a href="https://nors.ku.dk/publikationer/webpublikationer/muslimske_fornavne/">Muslimske fornavne i danmark</a>. Publisher: Københavns Universitet <ul> <li>minority_first_names_men.csv</li> <li>minority_first_names_men.csv</li> </ul> The list majority_unisex_names.csv is retrieved from <a href="https://familieretshuset.dk/navne/navne/godkendte-fornavne">The Agency of Family Law</a> in Denmark, and the numbers are retrieved from the above lists from Statistics Denmark. The list minority_last_names.csv is retrieved from <a href="https://www.familyeducation.com/baby-names/surname/origin/muslim">FamilyEducation</a>. The list overlapping_names.csv contains first names, which both occur on the list of majority names and the list of minority names.

Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"

These are data sets and tables used in the paper "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado" to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.

opencc-zeroMar 2023View details →

Data from: Longitudinal effects of early psychosocial deprivation on macaque executive function: Evidence from computational modelling

Executive function (EF) describes a group of cognitive processes underlying the organization and control of goal-directed behaviour. Environmental experience appears to play a crucial role in EF development, with early psychosocial deprivation often linked to EF impairment. However, many questions remain concerning the developmental trajectories of EF after exposure to deprivation, especially concerning specific mechanisms. Accordingly, using an 'A-not B' paradigm and a macaque model of early psychosocial deprivation, we investigated how early deprivation influences EF development longitudinally from adolescence into early adulthood. The contribution of working memory and inhibitory control mechanisms were examined specifically via the fitting of a computational model of decision-making to the choice behaviour of each individual. As predicted, peer-reared animals (i.e. those exposed to early psychosocial deprivation) performed worse than mother-reared animals across time, with the fitted model parameters yielding novel insights into the functional decomposition of group-level EF differences underlying task performance. Results indicated differential trajectories of inhibitory control and working memory development in the two groups. Such findings not only extend our knowledge of how early deprivation influences EF longitudinally, but also provide support for the utility of computational modelling to elucidate specific mechanisms linking early psychosocial deprivation to long-term poor outcomes.

opencc-zeroMar 2023View details →

Data for: Predicting age and mass at maturity from feeding behavior and diet in M. sexta: An empirical test of a life history model

Feeding for most animals involves bouts of active ingestion alternating with bouts of no ingestion. In insects, the temporal patterning of bouts varies widely with resource quality and is known to affect growth, development time, and fitness. However, the precise impacts of resource quality and feeding behavior on insect life history traits is poorly understood. To explore and better understand the connections between feeding behavior, resource quality and insect life history traits, we combined laboratory experiments with a recently proposed mechanistic model of insect growth and development for a larval herbivore, Manduca sexta. We ran feeding trials for 4th and 5th instar larvae across different diet types (two hostplants and artificial diet) and used these data to parameterize a joint model of age and mass at maturity that incorporates both insect feeding behavior and hormonal activity. We found that the estimated durations of both feeding and non-feeding bouts were significantly shorter on low- than on high-quality diets. We then explored how well the fitted model predicted historical out-of-sample data on age and mass of M. sexta. We found that the model accurately described qualitative outcomes for the out-of-sample data, notably that a low-quality diet results in reduced mass and later age at maturity compared to high-quality diets. Our results clearly demonstrate the importance of diet quality on multiple components of insect feeding behavior (feeding and non-feeding), and partially validate a joint model of insect life history. We discuss the implications of these findings with respect to insect herbivory and discuss ways in which our model could be improved or extended to other systems.

Accompanying empirical data for Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex"

This repository contains data accompanying: Kirchherr et al., 2023, "Bayesian multilevel hidden Markov models identify stable state dynamics in longitudinal recordings from macaque primary motor cortex". Data collection methods: Two adult female rhesus macaques (Macaca mulatta) trained on a reaching, and grasping, and placing task served as the subjects. The animal handling as well as surgical and experimental procedures complied with European guideline (2010/63/UE) and authorized by the French Ministry for Higher Education and Research (project # 2016112713202878) in force on the care and use of laboratory animals, and were approved by the ethics committee CELYNE (comité d’éthique Lyonnais pour les neurosciences expérimentale, C2EA 42). After initial training, we performed a sterile surgery to implant six floating multielectrode arrays (FMA, Microprobes for Life Science, Gaithersburg, MD, USA) in the right (monkey 1) or left (monkey 2) cortical hemisphere. Each array was comprised of 32 platinum/iridium electrodes (impedance 0.5 MΩ at 1 kHz) with lengths ranging from 1 to 6 mm, and with an inter-electrode spacing of 400 μm. One electrode array was implanted in the primary motor cortex (M1), two were implanted in the ventral premotor cortex (F5), one in the dorsal premotor cortex (F2), and two in the prefrontal cortex (45a and 46/12r), as estimated according to a previous magnetic resonance imaging scan. For the purposes of this study, we analyzed data from the M1 array of each monkey. The wideband neural signal (bandpass filtered at 0.1 to 7500 kHz) was recorded at 30 kS/s, and amplified and digitized (16-bit; 0.192 μV resolution) with an Intan Tech-based (Intan Technologies, Los Angeles, CA, USA) open source acquisition system (Open Ephys; Siegle et al. 2017). This system uses a 256-channel Intan RHD2000 series acquisition board and 32-channel headstages (RHD2132). Spike detection was performed offline using Trisdesclous (Garcia & Pouzat,2015). The common reference was removed to reduce ambient noise. Spikes were then detected from each electrode using a threshold of 2 times the median absolute deviation (MAD), and analyzed as multi-unit activity (MUA) in 10 ms bins. All electrodes in which at least one well-isolated spike waveform was detected were selected for the following analyses. We thus used a sample of 21 electrodes out of 32 for monkey 1, and 25 out of 32 electrodes for monkey 2. Custom made detection panels were used to record the moments when the monkey’s hand released the handle, the hand contacted the target object, and when the object was placed in the groove. An Omniplex 16-channel recording system (Plexon, Dallas, TX, USA) was used to simultaneously record these behavioral events. Trials were discarded if the response time (time between the go signal and handle release) was less than 100 or greater than 1500 ms, the reach duration (time between handle release and object contact) was less than 100 or greater than 1000 ms, or the placing duration (time between object contact and placing the object in the groove) was less than 100 or greater than 1200 ms, leaving 19 - 68 trials per day for monkey 1 (M = 43.9, SD = 15.46, N = 439; left: M = 14.8, SD = 5.74; center: M = 14.4, SD = 5.15; right: M = 14.7, SD = 7.73), and 23 - 49 per day for monkey 2 (M = 38.3, SD = 9.87, N = 383; left: M = 14.2, SD = 3.91; center: M = 10.8, SD = 3.55; right: M = 13.3, SD = 3.37). Abstract: Neural populations, rather than single neurons, may be the fundamental unit of cortical computation. Analyzing chronically recorded neural population activity is challenging not only because of the high dimensionality of activity in many neurons, but also because of changes in the recorded signal that may or may not be due to neural plasticity. Hidden Markov models (HMMs) are a promising technique for analyzing such data in terms of discrete, latent states, but previous approaches have either not considered the statistical properties of neural spiking data, have not been adaptable to longitudinal data, or have not modeled condition specific differences. We present a multilevel Bayesian HMM which addresses these shortcomings by incorporating multivariate Poisson log-normal emission probability distributions, multilevel parameter estimation, and trial-specific condition covariates. We applied this framework to multi-unit neural spiking data recorded using chronically implanted multi-electrode arrays from macaque primary motor cortex during a cued reaching, grasping, and placing task. We show that the model identifies latent neural population states which are tightly linked to behavioral events, despite the model being trained without any information about event timing. We show that these events represent specific spatiotemporal patterns of neural population activity and that their relationship to behavior is consistent over days of recording. The utility and stability of this approach is demonstrated using a previously learned task, but this multilevel Bayesian HMM framework would be especially suited for future studies of long-term plasticity in neural populations.

opencc-by-4.0Sep 2022View details →

Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."

Datasets used to train the models in "Deep learning for denoising High-Rate Global Navigation Satellite System data."  Additional information can be found at https://github.com/amtseismo/hrgnss_denoising.

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 2

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset and the Musall et al. mouse dataset processed by MINE. This set contains the last fish and the mouse MINE model weights. The other 24 fish are contained in Set 1.

CNN weight data for "Model identification of neural encoding (MINE)" publication - Set 1

This dataset contains the weights of fit CNN models generated during the analysis of the zebrafish thermoregulation dataset processed by MINE. This set contains 24/25 fish. The last fish and mouse MINE model weights are contained in Set 2.

Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment

Selection of the data in the following paper: Sengers, B. A. M., Steinfeld, G., Hulsman, P., & Kuehn, M. (2023). Validation of an interpretable data-driven wake model using lidar measurements from a free-field wake steering experiment. Wind Energy Science Discussions, 1-32. This data subset provides input parameters commonly used in wake models, as well as ten-minuted averaged cross sections of the flow field at 4 rotor diameters downstream, as measured by a nacelle-mounted lidar.  Cite this as: B.A.M. Sengers (2023). Dataset: Validation of an interpretable data-driven wake model using lidar measurements from a field wake steering experiment. https://doi.org/10.5281/zenodo.7741395

Data and code supporting Thomas & Yelland 2023 "Terrestrial Effects of Nearby Supernovae: Updated Modeling"

Data and code supporting publication Thomas & Yelland 2023 "Terrestrial Effects of Nearby Supernovae: Updated Modeling" Paper citation: Brian C. Thomas and Alexander M. Yelland 2023 ApJ 950 41 DOI: 10.3847/1538-4357/accf8a Contents of this repository: * Data files (in netCDF format) generated by GSFC 2D atmospheric chemistry-dynamics model, for cases described in referenced paper. * Code "SNCR_Flux_and_Ionization.m" used to calculate supernova cosmic ray proton flux and resulting terrestrial atmospheric ionization rates, for cases described in the referenced paper.  Also includes necessary input data files. * Data files (SNCR_flux.zip and SNCR_ionization.zip) generated by "SNCR_Flux_and_Ionization.m" code, as presented, discussed, and used in referenced paper. * Code "SNCR_muon-flux.m" used to calculate terrestrial surface-level cosmic ray secondary muon flux in the cases described in referenced paper, as well as biological damage values due to that flux.  Also includes necessary input data files. Code files were created and run in Octave, and should run on Matlab as well.

opencc-by-4.0Sep 2022View details →

CESM and FOCI model data as supplementary data for Climate Index Collection based on model data (CICMoD)

The Community Earth System Model (CESM) and the Flexible Ocean and Climate Infrastructure (FOCI) are both fully-coupled, global climate models that provide state-of-the-art computer simulations of the Earth's past, present, and future climate states. This dataset contains results from control runs with conditions of year 1850 without additional external forcing for 1000 years and 999 years for FOCI and CESM, respectively. Included features are: <ul> <li>sea surface temperature</li> <li>surface air temperature</li> <li>sea level pressure</li> <li>sea surface salinity</li> <li>geopotential height (500mb)</li> <li>precipitation</li> </ul>

opencc-by-4.0Apr 2023View details →

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"

General 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. 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): 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.", journal = "International Journal of Solids and Structures", year = "2023", volume = "283", pages = "112470", doi = "10.1016/j.ijsolstr.2023.112470", author = "Ling Wu, Cyrielle Anglade, Lucia Cobian, Miguel Monclus, Javier Segurado, Fatma Karayagiz, Ubiratan Freitas, and Ludovic Noels" 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. Description 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. Bibliography <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> Directories All the codes and experimental results are in five directories: <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> Figures of [WU23] <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>

opencc-by-4.0Dec 2021View details →

scMARK an 'MNIST' like benchmark to evaluate and optimize models for unifying scRNA data

Here we present a novel benchmark dataset (scMARK.v2), that consists of 11 published cancer scRNA-seq studies, for which we standardized  cell-type author labels and gene identifiers. scMARK.v2 can be used to ask how well models integrate data from different scRNA studies. We also provide a 12th standardized study (Wu et al 2021) that we held-out for evaluation of alignment of data "never seen" before, and a 13th study of newly generated in-vitro scRNA-seq data from cancer and fibroblast cells. <ul> <li>Data is provided as aData *h5ad files that can be read with Python's library <a href="https://scanpy.readthedocs.io/en/stable/">Scanpy</a>.</li> <li>Studies inclided in scMARK.v2 were downsampled to 10,000 cells per study.</li> <li>The difference between <a href="https://zenodo.org/record/5765804">scMARK.v1</a> and scMARK.v2, is that in v2, we provide at least two studies for each cancer type and each cell type; whereas in v1 a handfull of cell types were present only in one study.</li> </ul>

opencc-by-4.0Dec 2022View details →

AFRP22 Tomographic Model & MTZ Receiver Functions: Data, Software, Plotting

AFRP22 (Boyce et al., 2023), is an adaptively parameterized, absolute P-wave tomographic model for Africa provided with and without crustal correction. Also provided is mantle transition zone Receiver Functions for new stations in the Turkana Depression (data embargo lifted 2024-05-24). Citation: Boyce, A., Kounoudis, R., Bastow, I. D., Cottaar, S., Ebinger, C. J., Ogden, C. S. Mantle Wavespeed and Discontinuity Structure below East Africa: Implications for Cenozoic Hotspot Tectonism and the Development of the Turkana Depression (under revision) Geochemistry, Geophysics, Geosystems. This repository accompanies the publication of the tomographic model on the IRIS-EMC that can be found here: https://ds.iris.edu/ds/products/emc-afrp22/ or with the following doi: <a href="https://doi.org/10.17611/dp/emc.2023.afrp22.1">https://doi.org/10.17611/dp/emc.2023.afrp22.1</a>. Please find AFRP22 and its related files for a detailed description of the distributed model. The AFRP22_RFs_SHARE.tar repository contains: <ul> <li>Plotting: Codes and necessary files to reproduce figures in main manuscript.</li> <li>tomography <ul> <li>Tomography_AFRP22: Implementation of MIT inversion algorithm used to produce AFRP22 including formatted data.</li> <li>Raw_data: Hand-picked and processed seismic data (.SAC) from new African seismic networks.</li> <li>Original_documentation: Documentation for original distribution of inversion code from MIT global seismology group </li> </ul> </li> <li>receiver_functions <ul> <li>Processing codes (SMURFPy) already available at doi: <a href="https://doi.org/10.5281/zenodo.4337257">10.5281/zenodo.4337257</a></li> <li>Raw_data - Data files for P, PP and PKP RFs, CCP stacked volumes produced by SMURFPy code and picked discontinuity files</li> </ul> </li> </ul> The following authors contributed to this work: A. Boyce: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK. R. Kounoudis: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK. I. D. Bastow: Department of Earth Science and Engineering, Royal School of Mines, Prince Consort Road, Imperial College London, London, UK. S. Cottaar: University of Cambridge, Department of Earth Science, Bullard Laboratories, Madingley Road, Cambridge, UK. C. J. Ebinger: Department of Earth and Environmental Sciences, Tulane University, New Orleans, LA 70118, USA. C. S. Ogden: School of Geography and Geology, University of Leicester, Leicester, LE1 7RH, UK.

opencc-by-4.0Feb 2021View details →

Pyrolysis Model Data Set Contribution for the MaCFP Workshop April 2021 - Dataset

This is a contribution of material parameters for pyolysis modelling, to the <a href="https://iafss.org/macfp/">MaCFP Workshop in April 2021</a>.   This repository contains the input files for the inverse modelling process (IMP), the data bases with the IMP results, the analysis scripts and the simulation data of the requested model predictions. Different approaches were followed and all their data is stored here. Only two of them were presented to the MaCFP Workshop and were labelled "Approach A" and "Approach B" in the submitted report. Note however, that the labelling of the conducted IMP's is different. <ul> <li>Approach A: R2_CAPAII_UMD_60_from_TGA_LCPP (Case A2)</li> <li>Approach B: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03 (Case B2)</li> </ul>   An intermediate naming convention was introduced as follows: <ul> <li>Case A1: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_LCPP</li> <li>Case A2: TGA: R2_CAPAII_UMD_25_60_from_TGA_LCPP, CAPA II: R2_CAPAII_UMD_60_from_TGA_LCPP</li> <li>Case A3: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_25_60_from_TGA_UMET</li> <li>Case A4: TGA: R2_TGA_UMET_1_10_50_Methane_Scen_00_neu, CAPA II: R2_CAPAII_UMD_60_from_TGA_UMET</li> <li>Case B1: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_02</li> <li>Case B2: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_range_rep_03</li> <li>Case B3: TGA and CAPA II: R2_CAPAII_UMD_25_60_TGA_LCPP_2_5_15_20_rep_02</li> </ul> Note: The two archives mimic the directory structure of the project, where both are at the same lavel next to each other. If you would want to run the analysis scripts the relative file paths should work out of the box.   Version 1.1: Added description and README.   Version 1.2: Added archive containing the FDS input and output of approaches A and B directly (one does not need to download the full IMP_Runs21.rar), as well as the files contributed to the MaCFP-2 workshop.

Insights into the Magmatic Feeding System of the 2021 Eruption at Cumbre Vieja (La Palma, Canary Islands) Inferred from Gravity Data Modeling. Remote Sens. 2023, 15, 1936. https://doi.org/10.3390/rs15071936

Paper: Insights into the magmatic feeding system of the 2021 eruption at Cumbre Vieja (La Palma, Canary Islands) inferred from gravity data modeling  F. G. Montesinos1,7, S. Sainz-Maza2,7, D. Gómez-Ortiz3, J. Arnoso4,7, I. Blanco-Montenegro5,7, M. Benavent1,7 E. Vélez4,7, N. Sánchez6 and T. Martín-Crespo3 1 Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Plaza de Ciencias 3, 28040 Madrid, Spain. 2 Observatorio Geofísico Central (IGN). C/ Alfonso XII, 3. 28014 Madrid, Spain. 3 Dpt. Biología y Geología, Física y Química Inorgánica, ESCET, Universidad Rey Juan Carlos. C/Tulipán s/n, 28933 Móstoles, Madrid, Spain. 4 Instituto de Geociencias (IGEO), CSIC-UCM. C/ Doctor Severo Ochoa, 7. 28040 Madrid, Spain. 5 Departamento de Física, Escuela Politécnica Superior, Universidad de Burgos. Avda. de Cantabria s/n, 09006 Burgos, Spain. 6 Instituto Geológico y Minero de España (IGME, CSIC), Unidad Territorial de Canarias, Alonso Alvarado, 43, 2A, 35003 Las Palmas de Gran Canaria, Spain. 7 Research Group ‘Geodesia’, Universidad Complutense de Madrid, Spain. Corresponding author: Fuensanta G. Montesinos (fuensant@ucm.es) This research is supported by the project PID2019-104726GB-I00/AEI/10.13039/501100011033 funded by the Spanish Research Agency. Further, the University Complutense of Madrid (grants Financiación Grupos 2021, UCM 2022-GRFN14/22) and the Spanish Ministry of Science and Innovation (RD 1078/2021, funding for research activities of the CSIC-PIE project CSIC-LAPALMA-07) supported this research. ------------------------------------------------------------------------------------------------ Responsible Researchers: - Fuensanta González Montesinos, Facultad de CC. Matemáticas, Universidad Complutense de Madrid. Spain fuensant@ucm.esResponsible Researchers:  -  José Arnoso Sampedro,  Instituto de Geociencias (CSIC-UCM), Spain jose_arnoso@csic.es   >> The use of this data set is limited to academic or research purposes and it have to be referenced Zone:Cumbre Vieja (La Palma Island, Spain) Geodetic Coordinates Datum WGS84 Gravity(mGal) and Bouguer Gravity anomaly GRS80 (mGal)(Terrain density 2450 kg/m3) The file GravityCumbreVieja_FGMontesinos_et_al.dat includes the values of gravity and complete Bouguer gravity anomaly (GRS80) calculated for the land gravity stations at the Cumbre Vieja area (La Palma Island, Spain). The gravity values were observed in 142 land gravity stations (Figure 3 in the manuscript) by our group in 2005 and 2021 surveys The positions of the stations were selected to cover most of the Cumbre Vieja area, and the coordinates were obtained by differential GPS (WGS84 Datum). The gravity observations were processed taking into account the usual corrections (instrument height, drift, jumps, etc.). The tidal correction was calculated from gravity tide measurements made in several islands of the Canary Archipelago. All the gravity values referred to absolute gravity stations (Table S1). The procedure to obtain the terrain correction and the Bouguer anomaly map is explained in the manuscript and in the supporting information.