Data
Sloan-Digital-Sky-Survey-DR16

Sloan-Digital-Sky-Survey-DR16

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Feedback: Mukharbek Organokov organokov.mgmail.com Context Sloan Digital Sky Survey current DR16 Server Data release with Galaxies, Stars and Quasars. License: Creative Commons Attribution license (CC-BY) More datailes here. Find more here. Content The table results from a query which joins two tables: "PhotoObj" which contains photometric data "SpecObj" which contains spectral data. 16 variables (double) and 1 additional variable (char) 'class'. A class object can be predicted from the other 16 variables. Variables description: objid = Object Identifier ra = J2000 Right Ascension (r-band) dec = J2000 Declination (r-band) u = better of deV/Exp magnitude fit (u-band) g = better of deV/Exp magnitude fit (g-band) r = better of deV/Exp magnitude fit (r-band) i = better of deV/Exp magnitude fit (i-band) z = better of deV/Exp magnitude fit (z-band) run = Run Number rerun = Rerun Number camcol = Camera column field = Field number specobjid = Object Identifier class = object class (galaxy, star or quasar object) redshift = Final Redshift plate = plate number mjd = MJD of observation fiberid = fiberID Comments A four-color UVGR intermediate-band photometric system (Thuan-Gunn astronomic magnitude system) is discussed in [1]. The Sloan Digital Sky Survey (SDSS) photometric system, a new five-color (u g r i z) wide-band CCD system is described in [2] The variables 'run', 'rerun', 'camcol' and 'field' features which describe a field within an image taken by the SDSS. A field is basically a part of the entire image corresponding to 2048 by 1489 pixels. A field can be identified by: - run number, which identifies the specific scan, - the camera column, or "camcol," a number from 1 to 6, identifying the scanline within the run, and the field number. The field number typically starts at 11 (after an initial rampup time), and can be as large as 800 for particularly long runs. - An additional number, rerun, specifies how the image was processed. The variable 'class' identifies an object to be either a galaxy (GALAXY), star (STAR) or quasar (QSO). References: [1] Thuan Gunn (1976, PASP, 88,543) [2] Fukugita, M. et al, Astronomical J. v.111, p.1748 Data server Data can be obtained using SkyServer SQL Search with the command below: -- This query does a table JOIN between the imaging (PhotoObj) and spectra -- (SpecObj) tables and includes the necessary columns in the SELECT to upload -- the results to the SAS (Science Archive Server) for FITS file retrieval. SELECT TOP 100000 p.objid,p.ra,p.dec,p.u,p.g,p.r,p.i,p.z, p.run, p.rerun, p.camcol, p.field, s.specobjid, s.class, s.z as redshift, s.plate, s.mjd, s.fiberid FROM PhotoObj AS p JOIN SpecObj AS s ON s.bestobjid = p.objid WHERE p.u BETWEEN 0 AND 19.6 AND g BETWEEN 0 AND 20 Learn how to. Some examples. Full SQL Tutorial. Or perform a complicated, CPU-intensive query of SDSS catalog data using CasJobs, SQL-based interface to the CAS. Acknowledgements SDSS collaboration. Inspiration The Sloan Digital Sky Survey has created the most detailed three-dimensional maps of the Universe ever made, with deep multi-color images of one-third of the sky, and spectra for more than three million astronomical objects. It allows to learn and explore all phases and surveys - past, present, and future - of the SDSS.

18 features

objidnumeric67951 unique values
0 missing
ranumeric99998 unique values
0 missing
decnumeric99998 unique values
0 missing
unumeric79840 unique values
0 missing
gnumeric85895 unique values
0 missing
rnumeric87969 unique values
0 missing
inumeric88623 unique values
0 missing
znumeric89451 unique values
0 missing
runnumeric516 unique values
0 missing
rerunnumeric1 unique values
0 missing
camcolnumeric6 unique values
0 missing
fieldnumeric840 unique values
0 missing
specobjidnumeric100000 unique values
0 missing
classstring3 unique values
0 missing
redshiftnumeric99298 unique values
0 missing
platenumeric6117 unique values
0 missing
mjdnumeric2167 unique values
0 missing
fiberidnumeric1000 unique values
0 missing

19 properties

100000
Number of instances (rows) of the dataset.
18
Number of attributes (columns) of the dataset.
Number of distinct values of the target attribute (if it is nominal).
0
Number of missing values in the dataset.
0
Number of instances with at least one value missing.
17
Number of numeric attributes.
0
Number of nominal attributes.
0
Percentage of binary attributes.
0
Percentage of instances having missing values.
0
Percentage of missing values.
Average class difference between consecutive instances.
94.44
Percentage of numeric attributes.
0
Number of attributes divided by the number of instances.
0
Percentage of nominal attributes.
Percentage of instances belonging to the most frequent class.
Number of instances belonging to the most frequent class.
Percentage of instances belonging to the least frequent class.
Number of instances belonging to the least frequent class.
0
Number of binary attributes.

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