Find research datasets worth reusing

opencc-by-4.0Feb 2022View details →

MCMC chain of Milky Way gravitational potential models from McMillan (2017, MNRAS, 465, 76)

These are the full MCMC chains used for the main suite of results from McMillan (2017, MNRAS, 465, 76). Each line gives the parameters of a single model, with some of its derived properties, and an associated weight (the number of steps that the chain stayed at this model). The parameters are described in the README file, and further detail can be found in the original paper. The disc density profiles are of the form \(\begin{equation} \rho_d(R,z)=\left\{\begin{array}{lc}\frac{\Sigma(R)}{2z_d}\,\textrm{exp}\left(\frac{-\mid z\mid}{z_d}\right) & \textrm{for }z_d > 0 \\ \frac{\Sigma(R)}{4(-z_d)}\,\textrm{sech}^2\left(\frac{z}{2\,z_d}\right) & \textrm{for } z_d < 0,\\\end{array}\right. \end{equation}\) where \(\begin{equation} \Sigma(R)=\Sigma_0\;\textrm{exp}\left(-\frac{R_0}{R}-\frac{R}{R_d}+ \epsilon\textrm{cos}\left(\frac{\pi R}{R_d}\right)\right), \end{equation} \) with parameters \(\Sigma_0, R_d, z_d, R_0, \epsilon\) (note that \(R_0\) here is not the position of the Sun, and that \(\epsilon\) is not used). Spheroids have  \(\begin{equation} \rho_s=\frac{\rho_0}{(r^\prime/r_0)^\gamma(1+r^\prime/r_0)^{\beta-\gamma}}\; \textrm{exp}\left[-\left(r^\prime/r_{cut}\right)^2\right], \end{equation} \) where \(\begin{equation} r^\prime = \sqrt{R^2 + (z/q)^2} \end{equation} \) with parameters \( \rho_0, q, \gamma, \beta, r_0, r_{cut}\) (note that \(r_0\) is different again)

opencc-by-4.0Sep 2022View details →

MCMC chains for demographic fits presented in "NICMOS Kernel-Phase Interferometry II: Demographics of Nearby Brown Dwarfs"

These files are the data behind the figure for Figure 3 (and the corresponding Figure Set) as well as other fits presented in Table 5. They are saved in <a href="https://numpy.org/doc/stable/reference/generated/numpy.lib.format.html">npy</a> format which can be read into python using numpy according to the code snippet below. The files are flattened and trimmed MCMC chains produced by running emcee (Foreman-Mackey et al. 2013) using 64 walkers for 10,000 steps. The first 1,000 steps were trimmed for burn in and the remaining chains were thinned by 40 steps. The files are named according to the following convention: flatSamples<malm cor><age><prior>.npy where: <malm cor> is either 'Malm' or '' (nothing) if the model population was or was not corrected for Malmquist bias (before comparing to the observed population while fitting). <age> is '0p9', '1p2', '1p5', '1p9', '2p4', or '3p1' according to that assumed field age (in Gyr). <prior> is 'U' or 'I' for uninformed or informed (incorporating the information from Blake et al. 2010 on the unresolved population). The true underlying population corresponds to the flatSamplesMalm<age>I.npy files while the others are included for context and comparison to populations fit to the observed (not Malmquist corrected) population. The uninformed prior chains are dominated by a significant population of unresolved companions which is not consistent with previous RV studies. The files can be read into python using: <pre><code class="language-python">import numpy as np flat_samples0p9I = np.load('flatSamples0p9I.npy') </code></pre> which produces an array with shape 14400 x 4. The rows are the samples and the four columns are the parameters \(F, \gamma, \overline{\log(\rho)}\), and \(\sigma_{\log(\rho)}\), respectively.

opencc-by-3.0Sep 2022View details →

MCMC samples of the posterior distribution from the paper "TESS spots a mini-neptune interior to a hot saturn in the TOI-2000 system"

This dataset contains the Hamiltonian Monte Carlo samples of the posterior distribution of the planetary and stellar parameters from the paper "TESS Spots a Mini-Neptune Interior to a Hot Saturn in the TOI-2000 System". The file format, NetCDF, is based on HDF5, and is meant to be read by the Python package <a href="https://python.arviz.org/en/latest/">ArviZ</a>. Hot jupiters (P < 10 d, M > 60 M&oplus;) are almost always found alone around their stars, but four out of hundreds known have inner companion planets. These rare companions allow us to constrain the hot jupiter's formation history by ruling out high-eccentricity tidal migration. Less is known about inner companions to hot Saturn-mass planets. We report here the discovery of the TOI-2000 system, which features a hot Saturn-mass planet with a smaller inner companion. The mini-neptune TOI-2000 b (2.70 ± 0.15 R&oplus;, 11.0 ± 2.4 M&oplus;) is in a 3.10-day orbit, and the hot saturn TOI-2000 c (\(8.14^{+0.31}_{-0.30}\) R&oplus;, \(81.7^{+4.7}_{-4.6}\) M&oplus;) is in a 9.13-day orbit. Both planets transit their host star TOI-2000 (TIC 371188886, V = 10.98, TESS magnitude = 10.36), a metal-rich ([Fe/H] = \(0.439^{+0.041}_{-0.043}\)) G dwarf 174 pc away. TESS observed the two planets in sectors 9–11 and 36–38, and we followed up with ground-based photometry, spectroscopy, and speckle imaging. Radial velocities from HARPS allowed us to confirm both planets by direct mass measurement. In addition, we demonstrate constraining planetary and stellar parameters with MIST stellar evolutionary tracks through Hamiltonian Monte Carlo under the PyMC framework, achieving higher sampling efficiency and shorter run time compared to traditional Markov chain Monte Carlo. Having the brightest host star in the V band among similar systems, TOI-2000 b and c are superb candidates for atmospheric characterization by the JWST, which can potentially distinguish whether they formed together or TOI-2000 c swept along material during migration to form TOI-2000 b.

opencc-by-4.0Jul 2023View details →

The short gamma-ray burst population in a quasi-universal jet scenario: MCMC chains

The paper "The short gamma-ray burst population in a quasi-universal jet scenario" (https://arxiv.org/abs/2306.15488) described an effort in modelling the short gamma-ray burst population under the assumption that all jets share the same angular profile. This repository contains emcee hdf5 files with the MCMC chains corresponding to the "full sample" and "flux-limited sample" analyses described in the paper.

zenodo44/100

IPTA DR2 - GWB analysis MCMC output

IPTA DR2 common red noise, MCMC output   These files are the primary output from a Markov chain Monte Carlo (MCMC) sampling process. They are samples from the posterior probability distribution for a particular model described in the companion paper. Each zipped tarball contains four files.  The "chain" file has several tab-separated columns, each of which corresponds to a model parameter, except the last four which are metadata.  The parameter names (including metadata) are listed in the companion "params" file.  The frequencies used in the common red noise models are listed in the "crn_frequencies" file.  Additional information is provided in a README file. Each row of the chain file is one sample from the model posterior.  The first samples at the beginning of the MCMC are the "burn-in" phase, before the chain has converged to the posterior.  We recommend discarding the first ~25% of samples before using them to make inferences.

opencc-by-4.0Jan 2022View details →

zenodo44/100

SCIAMACHY NO regression fit MCMC samples

SCIAMACHY mesosphere NO data and regression model samples SCIAMACHY mesosphere daily zonal mean NO data and Markov-Chain Monte-Carlo samples from the regression coefficient distributions, derived from and for use with the <a href="https://github.com/st-bender/sciapy"><code>sciapy</code></a> regression module. This data set contains the following files: <ul> <li><code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float32.nc</code>, <code>NO_regress_output_pGM_Lya_ltcs_exp1dscan60d_km32_float64.nc</code> - samples from the regression coefficient distributions (single and double precision)</li> <li><code>NO_regress_quantiles_pGM_Lya_ltcs_exp1dscan60d_km32.nc</code> - the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions</li> <li><code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> - the SCIAMACHY daily zonal mean NO data</li> <li><code>sciapy_regress_tutorial.ipynb</code> - example ipython notebook</li> </ul> MCMC Samples The files <code>NO_regress_output..._float32.nc</code> and <code>NO_regress_output..._float64.nc</code> contain MCMC samples of the model as single and double precision floats. The file <code>NO_regress_quantiles....nc</code> contains the 0.1, 2.5, 16, 50, 84, 97.5, and 99.9 percentiles of the sampled distributions and is provided for convenience. The files contain the following parameters: <ul> <li><code>kernel:log_sigma</code>, <code>kernel:log_rho</code> - the "strength" and "lengthscale" of the Matérn-3/2 Gaussian Process kernel</li> <li><code>mean:offset:value</code> - the constant offset of the NO model in [\(10^6\) cm\(^{-3}\)]</li> <li><code>mean:Lya:amp</code> - the Lyman-\(\alpha\) coefficient of the mean model in [\(10^6\) cm\(^{-3}\) / Lyman-\(\alpha\)]</li> <li><code>mean:GM:amp</code> - the geomagnetic coefficient (AE) in [\(10^6\) cm\(^{-3}\) / nT]</li> <li><code>mean:GM:tau0</code> - the constant lifetime of the geomagnetic lifetime in [d]</li> <li><code>mean:GM:taucos1</code>, <code>mean:GM:tausin1</code> - cosine and sine amplitudes of the yearly geomagnetic lifetime variation in [d]</li> </ul> Daily zonal mean NO data The model was trained on the <a href="http://doi.org/10.5281/zenodo.1009078">SCIAMACHY mesosphere NO dataset</a>, binned into 10° geomagnetic latitude bins using the provided <code>gm_lat</code> variable and using the standard error of the mean as data uncertainties. The data are uploaded as <code>scia_nom_dzmNO_2002-2012_v6.2.1_2.2_akm0.002_geomag10_nw.nc</code> and were prepared by running (after installing <code><a href="https://github.com/st-bender/sciapy">sciapy</a>)</code>: <pre><code class="language-bash">bash> scia_daily_zonal_mean.py -g -b'-90:90:10' -o <daily_zonal_mean_NO.nc> </path/to/SCIAMACHY_NO_NOM_orbits_20??_v6.2.1.nc></code></pre> Regression sampling The samples were generated by running the following command: <pre><code class="language-bash">bash> python -m sciapy.regress <daily_zonal_mean_NO.nc> --proxies Lya:<Lyman-alpha_file.dat>,GM:<AE_file.dat> -A <altitude> -L <geomag_latitude_bin> -w 14 -b 800 -p 1400 -F \"\" -I GM --fit_annlifetimes GM --positive_proxies GM --lifetime_scan=60 --lifetime_prior exp -k -K Mat32 -O0 -m "nom_pGM_Lya_ltcs_exp1dscan60d_km32" -P</code></pre>

opencc-by-4.0Aug 2018View details →

zenodo44/100

MCMC simulations from the posterior distributions of European migration flows

This folder contains three RData files each containing MCMC simulations from the posterior distributions of a Bayesian hierarchal model used to estimate European migration flows, developed as part of the project Quantifying Migration Scenarios for Better Policy (QuantMig, <a href="https://eur03.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.quantmig.eu%2F&data=05%7C01%7CP.W.Smith%40soton.ac.uk%7Cc166e7bff7f840a7983b08db94af1244%7C4a5378f929f44d3ebe89669d03ada9d8%7C0%7C0%7C638267252014422806%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=yMDYBngYz%2BnzvXmNj6MqseYwso0ICNbREE8TmqQ%2BRJ0%3D&reserved=0">www.quantmig.eu</a>); see Aristotelous, Smith and Bijak (2022) for details. The first set of estimates are disaggregated by origin, destination and time, a breakdown which we denote as ODT. The second and third sets of estimates are again disaggregated by origin, destination and time, but they are additionally disaggregated by other factors. The second set is further disaggregated by age and sex and the third by just birth region. We respectively denote these breakdowns as ODAST and ODBT. Also, included in this folder is an R script to calculate summaries of these posterior distributions (means, and lower and upper quartiles) and output them to csv files, which are also in the folder. For further details, see deliverable_6_4_v1_2.pdf also in the folder.

opencc-by-4.0Aug 2023View details →

opencc-by-4.0Dec 2020View details →

MCMC simulation data for AutoGibbs.jl benchmarks

Chains and diagnostic data for the test models of <a href="https://github.com/phipsgabler/AutoGibbs.jl">AutoGibbs.jl.</a> Includes four CSV files for each of the models: GMM, HMM, and IMM.   Files are all in long format. The files for chains, diagnostics, and sampling times all contain "model", "discrete_algorithm" (AG or PG), "continuous_algorithm" (always HMC), "particles" (always 100), "data_size" (number of observations: 10, 25, or 50), and "repetition" (index of chain; there are 10 chains per parameter combination, except for HMC, there the benchmark has been killed somewhere after the eight chain). In addition to that, the sampling time files contain a column "samping_time" (in seconds), and the diagnostic files have columns "parameter" (name of the random variable), "diagnostic" (ESS or R_hat), and "value" with chain diagnostic values. Chain files have "parameter", "step", and "value" for the sampled values at each step of the chain.   The compile time measurements consist just of columns "model", "data_size", "repetition", "compilation_time" (in seconds).   Note that the "parameter" column in the HMM data has been manually adjusted; see the file plotting.jl in the AutoGibbs.jl repository.

opencc-by-4.0Nov 2020View details →

Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant - Datasets, Trained Models, BNN Samples, and MCMC Chains

We publish the training/validation/test datasets, trained model weights, configuration files, Bayesian neural network samples, and MCMC chains used to produce the figures in the LSST DESC paper, "Large-Scale Gravitational Lens Modeling with Bayesian Neural Networks for Accurate and Precise Inference of the Hubble Constant." They are formatted to be used with the DESC package "H0rton" (<a href="https://github.com/jiwoncpark/h0rton">https://github.com/jiwoncpark/h0rton</a>). Additional descriptions can be found in the README. Please contact Ji Won Park (@jiwoncpark) on GitHub or <a href="https://github.com/jiwoncpark/h0rton/issues">make an issue</a> for any questions.

opencc-zeroMay 2016View details →

MCMC chains for distance and structural parameters of Pisces A&B dwarf galaxies

These are Markov Chain Monte Carlo chains for Hubble Space Telescope observations of the dwarf galaxies Pisces A&B.  More details on how they are derived will be provided in a forthcoming ApJ paper.

opencc-by-4.0Mar 2022View details →

Nimble Output for MCMC Estimation of Source Apportionment; Accompaniment for Journal Article

Paper A dependent Bayesian Dirichlet Process model for source apportionment of particle number size distribution contributed to the Environmetrics Special Issue on Environmental Data Science. In this paper, a fairly extensive Bayesian model is run using the Nimble probabilistic programming language. To allow for user reproducibility, the output of this model (full, not summary), is provided here.

opencc-by-4.0Jun 2019View details →

Updated MCMC chains and subsamples for Hector calibration paper

These csvs contain MCMC chains and sampled subsets for calibration of the Hector simple climate model (<a href="https://github.com/JGCRI/hector">https://github.com/JGCRI/hector</a>, DOI:10.5194/gmd-8-939-2015). This series of five calibrations with different observational constraints and free parameters is described in Vega-Westhoff et al. (2019, DOI:10.1029/2018EF001082; see their table 1). The calibrations use a version of Hector that includes the BRICK sea-level module (<a href="https://github.com/scrim-network/BRICK">https://github.com/scrim-network/BRICK</a>, DOI:10.5194/gmd-10-2741-2017). Hector with BRICK is available on my fork of the Hector model (https://github.com/bvegawe/hector/tree/dev_slr). The calibration process is also adapted from BRICK. The code used to produce these chains can be found at https://github.com/bvegawe/hector_probabilistic, DOI:10.5281/zenodo.3236411. These five sets of MCMC chains were produced using hector_calib_driver.R. Inputs used to create each calibration are specified below:  T.csv:  Rscript hector_calib_driver.R -f *output folder* -n 200000 --forcing TRUE --np 10 --model_set onlyT_model --obs_set onlyT_obs TOHC.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set doeclim_model --obs_set doeclim_obs TOHCGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set noTE_model --obs_set noTE_obs TTE.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set onlyTE_model --obs_set doeclimTE_obs TTEGICGISAIS.csv: Rscript hector_calib_driver.R -f *output folder* --forcing TRUE --np 10 --model_set all_model --obs_set noOcheat_obs   V1.1 - Updated with new calibrations. Previously only included two calibration files, <a href="https://zenodo.org/api/files/5f877c29-3990-4d12-92f0-1a7c6f7d271c/wSLR_calib.csv?versionId=b9c5ea10-6f52-4b91-b30a-be68cb58fba6">wSLR_calib.csv</a>, which was a 'full' model calibration, including both ocean heat and thermosteric sea level historical constraints, and woSLR_calib.csv, which included only ocean heat and temperature constraints. After review, we redid the calibration experiment, including additional combinations of constraints, and removing the 'full' calibration, which treated the ocean heat and thermosteric sea level constraints as independent.

opencc-by-4.0Aug 2024View details →

Results of GARPOS-MCMC v2.0.0 for GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI"

Contains the results of "<a href="https://doi.org/10.5281/zenodo.13316197">GARPOS-MCMC v2.0.0</a>" for the GNSS-A data publishied by the Japan Coast Guard (<a href="https://doi.org/10.5281/zenodo.6417480">https://doi.org/10.5281/zenodo.6417480</a>).  Please refer Watanabe et al. (preprint will be available soon) for the details for methods and figure captions. Contents: - Results of GARPOS-MCMC v2.0.0 for the foure SGO-A sites (FUKU/KUM2/MYGI/TOS2).  <ul> <li>File name contains the site name and campaign code <ul> <li>*-chain.csv: Values of MCMC samples </li> <li>*-chain.pdf: Plots of MCMC samples</li> <li>*-hist.pdf: Plots of distributions and histograms for the parameters </li> <li>*-m.p.dat: Model parameters for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>*-obs.csv: Acoustic data including residuals for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>*-percentile.csv: Percentiles of the posterior pdfs for each parameters</li> <li>*-res.dat: Positioning result for the maximum a posteriori sample in the series (not be used as the "result")</li> <li>files in fig/ show the travel time residuals and parameters for the maximum a posteriori sample in the series</li> </ul> </li> <li>Directory "tested models" contains the MCMC series with inverse temperature of 1/(log n).  <ul> <li>fig_searchres*png: Plots of relative WBIC values</li> <li>searchres-*csv: WBIC and relevant values for each model</li> <li>Others: same as above</li> </ul> </li> </ul>

opencc-by-4.0Aug 2021View details →

MCMC samples from an analysis reported in a paper titled "A Song of Neither Ice nor Fire: Temperature extremes had no impact on violent conflict among European societies during the 2nd millennium"

These data are posterior samples from an MCMC used to estimate parameters for a Bayesian state-space time-series regression model. They are products of an analysis reported in "A Song of Neither Ice nor Fire: Temperature extremes had no impact on violent conflict among European societies during the 2nd millennium", an academic paper that has recently been submitted for peer review. We have archived the MCMC samples here in order to facilitate peer review, replication, and open science. The samples are stored in .RData format (matrices when loaded into an R environment) and the R code used to produce them is provided in a GitHub repo (https://github.com/wccarleton/extreme-conflict). Each .RData file contains a matrix called "samples". The columns of the matrix are each an MCMC chain for a given (column name) model parameter monitored during the MCMC simulation. See the aforementioned GitHub repo for more details.

opencc-by-4.0Jun 2022View details →

MCMC samples for X-ray spectra fits summarised in the paper "A strangely light neutron star"

Posterior samples for NS mass and radius described based on fitting of XMM-Newton and Suzaku spectra of the CCO in HESS J1731-347 and described in the paper the paper  "A strangely light neutron star"  (DOI: <a href="https://doi.org/10.21203/rs.3.rs-1509469/v1">10.21203/rs.3.rs-1509469/v1</a>). Two files uploaded contain posterior samples based based  a) on fitting X-ray data alone using single temperature carbon atmosphere model and Gaia parallax priors (xray_only_carbatm.txt) b) on fitting the same X-ray data but including also all additional priors described in the main text and full priors on distance rather than inverted parallax value (full_priors_carbatm.txt). Note that initial version contained wrong file uploaded by error, so the correct file to use in this case is full_priors_carbatm_corr.txt. We urge, however, to use a) as a baseline, i.e. as input for incorporating of other constraints for two reasons: first, the procedure of incorporating other constraints adopted by us and you may be different, and second, this file contains relatively small number of samples as it was mainly meant as illustration putting our results in context of other constraints and to give an idea of impact which our measurement has on EOS selection for a particular family of EOSs, so the result would be different if other set of EOSs is considered.  in addition a file with updated weights for EOSs used by Dietrich et al 2021 (2020Sci...370.1450D, available on https://github.com/diettim/NMMA) which take into the account constrains from X-ray spectral fitting presented in our work for the CCO and constrains based on modeling of the 4U 1702-429 bursts reported by Nattila et al 2017 (2017A&A...608A..31N) are included in file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_cco_weights.txt. This file is based on and has the same syntax as file chiralEFT_MTOV_NICER_GW170817_AT2017gfo_weighting.dat provided by Dietrich et al 2021 available on https://github.com/diettim/NMMA along with the corresponding tabulated EOS files.

opencc-by-4.0Dec 2019View details →

NICER PSR J0030+0451 Illinois-Maryland MCMC Samples

Posterior samples from the Illinois-Maryland analysis of NICER data for PSR J0030+0451.

openmit-licenseMar 2016View details →

CCFs and MCMC samples for Gullikson et al. 2016b

This dataset holds all of the cross-correlation functions (CCFs) generated in Gullikson et al. (2016). (update link when I have it). The CCFs are stored as HDF5 files, and are split into 2 GB chunks such that they can go here on zenodo. You will likely want to combine them all together for each instrument. The filenames have the form 'INSTRUMENT_ccfs_n.h5' where n is an integer.   This dataset also holds the MCMC samples for the primary star mass, companion mass, and system age for all of the stars listed in Table 1 of the paper. This is again stored as an HDF5 file. Finally, the dataset contains MCMC samples from the posterior distributions that result from fitting the data to a variety of parametric distributions. They files are generated by the pymultinest code, and the samples can be recovered with the same code (See RealData.ipynb notebook in the github repository for details: https://github.com/kgullikson88/BinaryInference).

dryad36/100

Rare and widespread: Integrating Bayesian MCMC approaches, Sanger sequencing and Hyb-Seq phylogenomics to reconstruct the origin of the enigmatic Rand Flora genus Camptoloma

Premise Genera that are widespread but have a geographically discontinuous distribution and are represented by few species are intriguing. Did they achieve their disjunct distribution recently, or is it ancient in origin? Why are they species-poor? The Rand Flora is a continental-scale floristic pattern in which closely related species appear co-distributed in isolated regions over the edges of Africa and nearby archipelagos. Genus Camptoloma (Scrophulariaceae) is the most notable example, comprising three species isolated from each other at the ends of the African continent: C. canariense in the west, endemic to the Canary Islands; C. lyperiiflorum in the east, endemic to the Horn of Africa - Southern Arabia; and C. rotundifolia, restricted to Southern Africa. Methods Here, we employed Sanger sequencing of nuclear and plastid markers, together with genomic target sequencing of 2190 low-copy nuclear genes, to infer interspecies relationships and the position of Camptoloma within Scrophulariaceae, using supermatrix and multispecies-coalescent approaches. Lineage divergence times and ancestral ranges were inferred with Bayesian MCMC approaches. Population history was estimated with phylogeographic structured coalescent methods. Key Results Our results support C. rotundifolia as sister to the disjunct clade formed by C. canariense and C. lyperiiflorum. Stem divergence was dated in the Late Miocene, while the origin of extant diversification within the genus was inferred as Early Pliocene. Conclusions We show that the current disjunct distribution of Camptoloma across Africa was likely the result of fragmentation and extinction/population bottlenecking events associated to historical aridification cycles, consistent with the "climatic refugia" hypothesis.

opencc-zeroApr 2022View details →

opencc-by-nc-4.0Jul 2022View details →

MCMC results for GNSS-A data obtained at the SGO-A sites "TOS2", "KUM2", "FUKU", and "MYGI"

Contains the results of "<a href="https://doi.org/10.5281/zenodo.6825238">GARPOS-MCMC v1.0.0</a>" for the GNSS-A data publishied by the Japan Coast Guard (<a href="https://doi.org/10.5281/zenodo.6417480">https://doi.org/10.5281/zenodo.6417480</a>).  Please refer Watanabe et al. (2023, J. Geod., <a href="https://doi.org/10.1007/s00190-023-01774-6">https://doi.org/10.1007/s00190-023-01774-6</a>) for the details for methods and figure captions. Contents: <Direcotry name> - *01-DoubleGrad: Results obtained for the m100 model - *02-SingleGrad: Results obtained for the m101 model - *03-Alpha2Offset: Results obtained for the m102 model - x-alpha_correlation: Figures of artificially obtained correlation between the seafloor position and the sound speed perturbations.  1. MCMC figures <File description> - *-cahin-1.png: Series of MCMC samples (all) - *-histogram.png: Histogram and distribution of MCMC samples <Files> fig01-DoubleGrad.tar.gz  - fig01-DoubleGrad    - [SITE]      - *-cahin-1.png      - *-histogram.png fig02-SingleGrad.tar.gz  - fig02-SingleGrad    - [SITE]      - same as fig01 fig03-Alpha2Offset.tar.gz  - fig03-Alpha2Offset    - [SITE]      - same as fig01 x-alpha_correlation.tar.gz  - [SITE]*-corrXA.png 2. Result data <File description> - *-cahin.csv: All MCMC samples - *-hp.csv: Statistical information (percentiles) for hyperparameters - *-m.p.dat: Model parameter for MAP sample - *-obs.csv: Data and residuals for MAP sample - *-res.dat: Position solution for MAP sample - *t.s*.png: Residuals and perturbation model for MAP sample <Files> res01-DoubleGrad.tar.gz  - res01-DoubleGrad    - [SITE]      - *-cahin.csv      - *-hp.csv      - *-m.p.dat      - *-obs.csv      - *-res.dat      - fig        - *t.s*.png res02-SingleGrad.tar.gz  - res02-SingleGrad    - [SITE]      - same as res01 res03-Alpha2Offset.tar.gz  - res03-Alpha2Offset    - [SITE]      - same as res01