Best way to filter NED redshift data

[nearc]

i'm using http://ned.ipac.caltech.edu/ui/?q=byparams to extract redshift data. while I'm very happy there is now lots of data available, data requests need to be limited in size or you get an error. i would like to examine the patterns of redshift vs longitude, but that is too much data, i could breakdown the longitude into tiny pieces, maybe 5 degrees at a time, however, even after glued all those sliced together it would be too big for a spreadsheet. i was wondering if redshift data could be ranked in some way so i could choose the "best" data thus downloading a subset of the complete data. i realize some of this ranking might be subjective or at least different people will have different options on what is the most valid data, however, are there some characteristics to look for? for example:

Classified Extragalactic options i could choose between absorption line vs emission line, does one tend to be more precise than the other?

Unclassified Extragalactic options: radio, infrared, visual, UV, x-ray or gamma-ray sources

Components: is a star better than a galaxy?

i realize this is a broad question and a little vague, but the goal is simple how to make a plot of redshift vs longitude with a manageable size of data that will represent the overall patterns of a plot of redshift vs longitude from the complete dataset. the items above are just some of the options if you have a better way to make the data size more manageable let me know

 

[gtoguy97]

.In terms of ranking redshift data, the most reliable sources of data are usually those that use direct or indirect methods such as spectroscopy and photometry to measure redshifts. These methods are the most precise and accurate, and have been tested and verified for accuracy. Absorption line spectroscopy is usually considered the most reliable method for measuring redshifts, since it provides an exact measurement of the redshift at a specific point in time as opposed to other methods which may not be as accurate.In terms of classification, galaxies are generally more reliable than stars, since their redshift values tend to be more consistent and can be measured more accurately. However, if you are looking for precision and accuracy, it is important to look at both individual stars and galaxies to ensure that you are getting the most accurate measurements.Finally, it is important to consider the sources of the data when ranking them. Are they from trusted sources? How recently were the measurements taken? Are the measurements supported by other measurements? All of these factors can help you decide which data points are the most reliable.