Describing / fitting a highly skewed distribution I've got a data set of 84,529 entries, each entry referring to the number of times a particular entry is cited in a database. This set is extremely skewed, ranging from entries with 0 citations to one with over 45,000 citations alone. The median of the set is 16 citations, and it can be shown that a relatively small number of elements disproportionally account for the vast majority of citations. The data set is here and a log(x+1) histogram is shown below.

My perhaps stupid question is whether there's a standard way to describe this particular distribution? Naively, I figured it might be a generalised pareto distribution given the dominance of a small number of terms, but toying with log-log plots didn't convince me of this but I don't know enough about them to assert anything confidently.

You should consider comparing distributions. One handy Python library to compare distributions to describing empirical data is the powerlaw library. It has a paper that explains how to use it:
import powerlaw
import pandas as pd 

data = pd.read_csv("sampledata.txt", header=None)

fit = powerlaw.Fit(data[0].values)

ax = fit.plot_ccdf(marker="o", ls="", ms=2, color="k")
fit.power_law.plot_ccdf(ax=ax, label="power law")
fit.truncated_power_law.plot_ccdf(ax=ax, label="truncated power law")
fit.lognormal.plot_ccdf(ax=ax, label="lognormal")
ax.legend()
ax.set_ylabel(r"$P(X\geq x)$")
ax.set_xlabel(r"$x$");


