Follow the Money (OpenTED)

Opening Up EU Procurement Data by Friedrich Lindenberg.

From the post:

What is the next European dataset that investigative journalists should look at? Back in 2012 at the DataHarvest conference, Brigitte, investigative superstar from FarmSubsidy and co-host of the conference, had a clear answer: let’s open up TED (Tenders Electronic Daily). TED is the EU’s shared procurement mechanism, and is at the heart of the EU contracting process. Opening it up would shine a light on the key questions of who receives public money, and what they receive it for.

Her suggestion triggered a two-year project, OpenTED, which, as of last week, has finally matured into a useful resource for journalists and researchers. While gaps remain, we hope it will now start to be used by journalists, NGOs, analysts and citizens to get information on everything from large scale trends to local municipal developments.

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TED collects tender notices for large public projects so that companies from all EU countries can bid on those contracts. For journalists, there are many exciting questions such a database would be able to answer: What major projects are being announced? Who is winning the contracts for these projects, and is that decision made prudently and impartially? Who are the biggest suppliers in a particular country or industry?

A data dictionary for the project remains unfinished and there are plenty of other opportunities to contribute to this project.

The phrase “large public project” means projects with budgets in excess of €200,000. If experience in the United States holds true for the EU, there can be a lot of FGC (Fraud, Greed, Corruption) in under €200,000 contracts.

If you are looking for volunteer opportunities, the data needs to be used and explored, a data dictionary remains unfinished, current code can be improved and I assume documentation would be appreciated.

Certainly the type of project that merits widespread public support.

I find the project interesting because once you connect the players based on this data set, folding in other sets of connections, such as school, social, club, agency, employer, will improve the value of the original data set. Topic maps of course being my preferred method for the folding.

I first saw this in a tweet by ePSIplatform.

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