# Softverse

Softverse counts how often each R, Python and Stata package is loaded by the
code behind published social science papers. It covers 3,223
packages across 8,349 replication deposits at economics and
political science journals that check an author's code before publication.

The counts exist to support credit, and they are built to be expensive to
fake. What a software author can point to today is a download count, which
records how often a file was fetched, or a mention in someone's methods
section. A build server installing a package on every commit runs the
download count up on its own, and cutting more frequent releases runs it up
further, so a number like that tells a hiring committee about release
practice as much as about use. Adding one to a count here takes a paper
published at a journal that checks its authors' code, with the package loaded
in that code.

## Three things the counts show

The most loaded package is `estout`, a Stata command that formats
regression output into a table, in 2,440 deposits. three
of the four most loaded packages also present results rather than estimate
them, so the tools research leans on hardest are the ones that prepare its
exhibits, and those are the tools least likely to be cited.

Stata is the most used of the three languages and the least studied. It
appears in 6,212 deposits against 3,518 for R. Work on research
software covers R and Python because CRAN and PyPI will tell you which
project a library name belongs to, and Stata has no equivalent registry. This
project reconstructed one from the Statistical Software Components archive
and released it.

Some heavily used software belongs to no registry at all. `grc1leg` is called
1,224 times in this corpus, StataCorp distributes it from their own
website, and no archive lists it, so a credit system that works by registry
lookup cannot reach whoever wrote it.

## Where to go

<a href="lookup/">Look up a package</a> to see its count, the denominator it
is a share of, and which of the two repositories is doing the crediting.

<a href="paper/softverse.pdf">Read the paper</a> for the sampling frame, the
extraction method, and the validation, which includes measuring the R
extractor against R's own parser.

<a href="data/">Take the data</a>, published as CSV under CC0, along with the
Stata command-to-package index at
[10.5281/zenodo.21926099](https://doi.org/10.5281/zenodo.21926099).

## What the counts do not show

These counts say a package was loaded by code in the deposit. They do not say
the code ran. Authors often leave older scripts in a deposit, and a script can
load a package inside a branch that never executes. Whatever an author kept
out of the deposit cannot be counted at all. The paper measures how wide that
gap is.

```{toctree}
:hidden:
:caption: Reference

data-dictionary
```

```{toctree}
:hidden:
:caption: Elsewhere

Source <https://github.com/recite/softverse>
Stata index (Zenodo) <https://doi.org/10.5281/zenodo.21926099>
```