A project to collect reports from the offices of Inspectors General across the US federal government.

For more information about the project, read:

• Opening up government reports through teamwork and open data
• Why we've collected a hojillion inspector general reports

From one of the above pieces:

Just about every agency in the federal government has an independent unit, usually called the Office of the Inspector General, dedicated to independent oversight. This includes regular audits of the agency's spending, monitoring of active government contractors and investigations into wasteful or corrupt agency practices. They ask tough questions, carry guns, and sue people.

The initial round of writing scrapers for all 65 federal IGs has come to a close. However, there are two important areas we need help in:

• Keeping the scrapers working. They're scrapers: they break. Check the Oversight.garden dashboard for scrapers in need of attention, or check the issues list for other tasks.

Ask @konklone for an invitation to the project Slack if you want to talk with teammates and get involved.

• Just as importantly, sending in reports we can't scrape.

There are 9 IGs who do not publish reports online, many from the US government's intelligence community.

• Architect of the Capitol
• Capitol Police
• Central Intelligence Agency
• Defense Intelligence Agency
• National Geospatial-Intelligence Agency
• National Reconnaissance Office
• National Security Agency
• Intelligence Community (ODNI)

Generally, getting their reports means filing Freedom of Information Act requests, or finding the results of FOIA requests others have already made.

We also need unpublished reports from the other 65 IGs! We're scraping what they publish online, but most IGs do not proactively publish all of their reports.

We don't yet have a formal process for submitting reports — for now, either open an issue and post a link to the file, or email the report to [email protected].

Python 3: This project uses Python 3, and is tested on Python 3.4.0. If you don't have Python 3 installed, check out pyenv and pyenv-virtualenvwrapper for easily installing and switching between multiple versions of Python.

Dependencies:

• To extract PDFs (the most common type of report), you'll need pdftotext, pdfinfo, and qpdf. On Ubuntu, apt-get install poppler-utils qpdf. On OS X, brew install poppler qpdf.
• To extract DOCs, you'll need abiword, which you can install via apt-get or brew.
• Install all the PIP dependencies by running pip install -r requirements.txt

To run an individual IG scraper, just execute its file directly. For example:

./inspectors/usps.py

This will fetch the current year's reports from the Inspector General for the US Postal Service and write them to disk, along with JSON metadata.

If you want to go back further, use --since or --year to specify a year or range:

./inspectors/usps.py --since=2009

If you want to run multiple IG scrapers in a row, use the igs script:

./igs

By default, the igs script runs all scrapers. It takes the following arguments:

• --safe: Limit scrapers to those declared in safe.yml. The idea is for "safe" scrapers to be appropriate for clients who wish to fully automate their report pipeline, without human intervention when new IGs are added, in a stable way.
• --only: Limit scrapers to a comma-separated list of names. For example, --only=opm,epa will run inspectors/opm.py and inspectors/epa.py in turn.
• --data-directory: The directory path to store the output files. Defaults to data in the current working directory.

Reports are broken up by IG and by year. So a USPS IG report from 2013 with a scraper-determined ID of no-ar-13-010 will create the following files:

/data/usps/2013/no-ar-13-010/report.json
/data/usps/2013/no-ar-13-010/report.pdf
/data/usps/2013/no-ar-13-010/report.txt

Metadata for a report is at report.json. The original report will be saved at report.pdf (the extension will match the original, it may not be .pdf). The text from the report will be extracted to report.txt.

Every scraper will accept the following options:

• --year: A YYYY year, only fetch reports from this year.
• --since: A YYYY year, only fetch reports from this year onwards.
• --debug: Print extra output to STDOUT. (Can be quite verbose when downloading.)
• --dry_run: Will scrape sites and write JSON metadata to disk, but won't download full reports or extract text.

Every report has an accompanying JSON file with metadata. That JSON file is an object with the following required fields:

• inspector - The handle you chose for the IG. e.g. "usps"
• inspector_url - The IG's primary website URL.
• agency - The handle of the agency the report relates to. This can be the same value as inspector, but it may differ -- some IGs monitor multiple agencies.
• agency_name - The full text name of an agency, e.g. "United States Postal Service"
• report_id - A string usable as an ID for the report.
• title - Title of report.
• published_on - Date of publication, in YYYY-MM-DD format.

Additionally, some information about report URLs is required. However, not all report contents are released: some are sensitive or classified, or require a FOIA request to obtain. Use these fields to handle report URLs:

• url - URL to the report itself. Required unless unreleased is True.
• landing_url - URL to some kind of landing page for the report.
• unreleased - Set to True if the report's contents are not fully released.

If unreleased is True, then url is optional and landing_url is required.

The JSON file may have arbitrary additional fields the scraper author thought worth keeping.

The report_id must be unique within that IG, and should be stable and idempotent.

This project's chief maintainer, Eric Mill, runs a copy of this project on a server that automatically backs up the downloaded bulk data.

Data is backed up to the Internet Archive.

To back up individual reports as items in the collection, run the backup script:

./backup

This goes through all reports in data/ for which a report has been released (in other words, where unreleased is not true), and uploads their metadata and report data to the Internet Archive.

For example, the treasury IG's 2014 report OIG-14-023 report can be found at:

https://archive.org/details/us-inspectors-general.treasury-2014-OIG-14-023

To generate bulk data, the following command is run from the project's output data/ directory.

zip -r ../us-inspectors-general.bulk.zip * -x "*.done"
cd ..
./backup --bulk=us-inspectors-general.bulk.zip

Both zipping and uploading take a long time -- this is a several-hour process at minimum.

The process zips up the contents of the data/ directory, while excluding any .done files that track the status of individual file backups. The zip file is placed up one directory, so that it doesn't interfere with the automatic directory examination of data/ that many scripts employ.

Then the file is uploaded to the Internet Archive as part of the collection, to be a convenient bulk mirror of the entire thing.

[TBD: Proper collection landing page, and bulk data link.]

• Matt Rumsey kindly compiled a spreadsheet of IG offices. We used this to track activity during the initial scraping phase.

This project is dedicated to the public domain. As spelled out in CONTRIBUTING:

The project is in the public domain within the United States, and copyright and related rights in the work worldwide are waived through the CC0 1.0 Universal public domain dedication.

All contributions to this project will be released under the CC0 dedication. By submitting a pull request, you are agreeing to comply with this waiver of copyright interest.