A collection of Python scripts to download, cleanup, and somewhat structure the RePEc dataset. The data is downloaded into an SQLite database, so it is possible to use SQL queries to analyse the data. E.g.,

SELECT count(*) FROM papers JOIN papers_jel USING (pid) WHERE year = 2010  AND code = 'D43'

will show how many papers were written in 2010 about oligopolistic markets.

Run

python main.py init
python main.py update

to setup an empty SQLite database, repec.db, and to download the full RePEc dataset into it (takes a while). See

python main.py init --help
python main.py update --help

for available options.

The scripts use cld2-cffi for automatic language detection, and curl for downloading from FTP sites. curl is used instead of requests, because requests cannot handle some of the FTP sites out there.

The script downloads the data using breadth-first approach. First, the names of all the available ReDIF files are downloaded from the RePEc FTP and saved in table repec. Second, all the files from table repec are downloaded from the RePEc FTP and are used to fill in table series. Among other data, table series will contain URLs where the data on particular series can be found. Unique URLs are then saved in table remotes. Third, all unique URLs will be visited to collect the listings of the final ReDIF documents. These listings are saved in table listings. Fourth, all the files from table listings are downloaded, processed, and saved in tables papers, authors, and papers_jel.

If an update is interrupted during the last stage, you can run

python main.py update --papers

and the update should resume from where it has stopped.

Incremental updates are currently not supported, however it is possible to perform a full update on an existing database. Paper records that are obsolete, i.e. those that can no longer be reached from the initial list of series from the RePEc FTP, are not pruned. This is done on purpose as on some days some participating websites work, and on other days they don't.

Downloaded records are saved as is in papers.redif (z-compressed). Additionally, the records are cleaned up and partially destructured into the respective fields. The cleanup steps include, among other:

• stripping html tags;
• language auto-detection (using cld2-cffi);
• jel codes extraction.

The SQLite database will contain the following tables.

• The other day, I made a web page where you can check trends in economics. It's like a toy version of google trends but then based on words from titles and abstracts from RePEc. Some trends are suggestive, e.g. it's all about new results.

There is also an official Perl script for downloading the data, see remi. Remi is aimed at downloading ReDIF files, whereas the current set of scripts is aimed at downloading and partially processing the files, with the idea of using an SQLite backend to track progress and to store the final results.