Data & Tutorials

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MSMA Datasets

These datasets represent a range of typical datasets encountered in modern astronomical research and are used throughout the MSMA volume. The MSMA R scripts obtain these datasets on-the-fly into the R session, so manual downloads are not necessary. However, they are available to the public for non-commercial educational or research use.

asteroid_dens.dat
censor_Be.dat
COMBO17_lowz.dat
GlobClus_M31.dat
GlobClus_MWG.dat
GlobClus_prop.dat
GX.dat
HIP.dat
HIP1.tsv
NGC4406_profile.dat
NGC4472_profile.dat
NGC4551_profile.dat
SDSS_17K.dat
SDSS_QSO.dat
SDSS_stars.csv
SDSS_test.csv
SDSS_wd.csv
Shapley_galaxy.dat
Webbink_GC_tab.txt

Astronomical datasets for statistical analysis

Univariate datasets

• Distance to the LMC (N=1, repeated measurements, measurement errors)
• Asteroid densities (N=28, density estimation, measurement errors)
• Globular cluster luminosity function (N~81+360, normal model, truncation, 2-sample test)
• Planetary nebula luminosity function (N~238+45+101+59, k-sample test, nonparametrics, semi-parametrics, truncation)

Censored datasets

• Planet host stars (N=39 and 29; survival analysis, Kaplan-Meier, 2-sample, linear regression, censoring)

Multivariate datasets

• Shapley galaxy redshift catalog (N=4,315, p=5, spatial point process, hierarchical clustering, wavelets, measurement errors)
• Hipparcos star catalog (N=2,719, p=8, multivariate clustering, mixture models, regression, measurement errors, outliers)
• COMBO-17 galaxy photometric catalog (N=3,462, p=65, multivariate analysis, regression, clustering, truncation)
• SDSS quasar catalog (N=46,420, p=23, multivariate analysis, regression, clustering, measurement errors, truncation, )
• New SDSS quasar catalog (N=77,429, p=18, multivariate analysis, regression, clustering, measurement errors, truncation, )

Signal processing (spectra and time series)

• Chandra Orion star flares (N=209, 678 and 14,258, time series analysis, inhomogeneous Poisson process, Bayesian modeling, wavelets)
• Extrasolar planet radial velocities (N=17, 52, 138, periodicity detection, nonlinear regression, parameter estimation, measurement error, Bayesian modeling)
• Galaxy spectra (feature characterization, parameter estimation)
• Quasar absorption spectra (feature characterization, parameter estimation, normal mixture model)

Model selection

• Gamma-ray burst afterglow modelling (N=63, p=2, linear regression, breakpoints, measurement errors, model elaboration and parsimony)

Images

• Chandra star cluster (Poisson signal detection, Poisson image deconvolution, parameter estimation)

Questions about these datasets may be directed to Eric Feigelson