  Rank/Select dictionaries are data structures for an ordered set $S \subset
\{0,1,...,n-1\}$ to compute $\rank(x,S)$ (the number of elements in $S$ which
are no greater than $x$), and $\select(i,S)$ (the $i$-th smallest element in
$S$), which are the fundamental components of \emph{succinct data structures}
of strings, trees, graphs, etc. In those data structures, however, only
asymptotic behavior has been considered and their performance for real data is
not satisfactory. In this paper, we propose novel four Rank/Select
dictionaries, esp, recrank, vcode and sdarray, each of which is small if the
number of elements in $S$ is small, and indeed close to $nH_0(S)$ ($H_0(S) \leq
1$ is the zero-th order \textit{empirical entropy} of $S$) in practice, and its
query time is superior to the previous ones. Experimental results reveal the
characteristics of our data structures and also show that these data structures
are superior to existing implementations in both size and query time.
