The intersection of civics & technology

Our community is growing rapidly these days. And while TransparencyCamp gave us a physical place to come together a few weeks ago, we’re still a little nomadic in the online world.

We’re also a very diverse group. The fact that we all often have to cross-post to the same set of lists indicates that we’ve got great inner communities that focus separately on coding, policy, social media, etc. is meant to be a neutral-ground home for the coder community in the open government world. Really, it’s just a links page. But it’s a links page with a nice Drupal theme you can proudly point to and say “this is my movement”. “Hacking” is, of course, a word with several meanings. In the programming world it is very much a positive term meaning something like “creative programming”.

The site is running Drupal and anyone that creates an account can edit any content on the site. So, it’s basically a new wiki.

Right now you can find:


Links to the primary convergence locations for this community, the Sunlight Labs and PoliParsers mail lists, the IRC channel #transparency, the Planet oGosh blog aggregator, the oGosh Facebook group, and the Upcoming Transparency Events page on the OpenCongress wiki.

Links to all of the other mail lists for our community (all of the one’s I’ve mailed here and some others).

Data & APIs

Then beginnings of a list of the databases and APIs that are available for government transparency data. If you’re a data source, add yourself to the list or make sure I got your entry correct, please.


Links to ongoing projects broken down by type-

– Open-source coding projects like OpenCongress and Sunlight’s
Fifty States.

– Policy projects like Open House/Senate.

– Wiki projects like the new Wired gov data wiki.

It was everything I could come up with quickly. I’ll be adding more as I see them, but feel free to add your own project.


Using a Drupal module you can enter the community’s IRC channel #transparency through the website.

Blog Aggregator

Recently I announced Planet oGosh, an aggregator bringing together a whole bunch of blogs in the open government tech community. I’m changing the URL to

Final Notes

Thanks to Kendall Clark for donating the domain name.

Bulk data downloads approved in the omnibus spending bill (success!)

Two recommendations of the Open House Project report have been taken up in the FY09 omnibus appropriations bill (H.R. 1105). The first recommendation in our chapter on legislative databases was that the Library of Congress make its bill status database directly available to the public and that the GPO not sell legislative documents to the public. These have been the two issues I’ve had my sights on over the last three years (probably starting here). The second recommendation was about coordinating web standards across Congress. These recommendations are addressed in two paragraphs the House statement accompanying the bill for Division G – Legislative Branch, which is almost like being law itself.

The two paragraphs were added by Congressman Mike Honda of California, one of our champions of the use of technology to further transparency and civic engagement. John Wonderlich of Sunlight Foundation, Rob Pierson in Honda’s office, and I collaborated on this over a long period of time. Honda got involved in 2007 asking the Library to look into this and then in 2008 getting the paragraphs added to the bill markup.

Continue reading “Bulk data downloads approved in the omnibus spending bill (success!)”

Congressional Transparency Limerick

There’s a wacky thread on the Open House List of poems for transparency, so I gave it a try. (In fairness, I should say I used an electronic pronunciation dictionary to find some of the rhymes.)

There once was a man named Mike Honda,
A congressman us geeks are quite fond ‘a,
In markup sessions takes on the chairman, a hulk,
so that we the people can get our data in bulk.

His friend maverick Joe likes transparency too,
Senate votes in XML he says long overdue,
At party politics he snorts,
Because the public should see those CRS reports.

And last we hear of the executive’s new plan,
For a CTO and CIO…
…perhaps YesWeScan?

Civic Hacking, the Semantic Web, and Visualization

Yesterday I held a session called Semantic Web II: Civic Hacking, the Semantic Web, and Visualization at Transparency Camp. In addition to posting my slides, here’s basically what I said during the talk (or, now on reflection, what I should have said):

Who I Am: I run the site which collects information on the status of bills in the U.S. Congress. I don’t make use of the semantic web to run the site, but as an experiment I generate a large semantic web database out of the data I collect, and some additional related data that I find interesting.

Data Isolation: What the semantic web addresses is data isolation. For instance, the website, which looks for correlations between campaign contributions to Members of Congress and how they voted on legislation, is essentially something that is too expensive to do for its own sake. Campaign data from the Federal Election Commission isn’t tied to roll call vote data from the House and Senate. It’s only because separate projects have, for independent reasons, massaged the existing data and made it more easily mashable that MAPLight is possible (that’s my site GovTrack and the site The semantic web wants to make this process cheaper by addressing mashability at the core. This is important for civic (i.e. political/government) data: machines help us sort, search, and transform information so we can learn something, which is good for civic education, journalism (government oversight), and research (health and economy). And it’s important for the data to be mashable by the public because uses of the data go beyond the resources, mission, and mandate of government agencies.

Beyond Metadata: We can think of the semantic web as going beyond metadata if we think of metadata as tabular, isolated data sets. The semantic web helps us encode non-tabular, non-hierarchical data. It lets us make a web of knowledge about the real world, connecting entities like bills in congress with members of congress, what districts they represent, etc. We establish relations like sponsorship, represents, voted.

Why I care: Machine processing of knowledge combined with machine processing of language is going to radically and fundamentally transform the way we learn, communicate, and live. But this is far off still. (This explains why I study linguistics…)

Then there are some slides on URIs and RDF.

My Cloud: When the data gets too big, it’s hard to remember the exact relations between the entities represented in the data set, so I start to think of my semantic web data as several clouds. One cloud is the data I generate from GovTrack, which is 13 million triples about legislation and politicians. Another cloud is data I generate about campaign contributions: 18 million triples. A third data set is census data: 1 billion triples. I’ve related the clouds together so we can take interesting slices through it and ask questions: how did politicians vote on bills, what are the census statistics of the districts represented by congressmen, are votes correlated with campaign contributions aggregted by zipcode, are campaign contributions by zipcode correlated with census statistics for the zipcode (ZCTA), etc. Once the semantic web framework is in place, the marginal cost of asking a new question is much lower. We don’t need to go through the work that MAPLight did each time we want a new correlation.

Linked Open Data (LOD): I showed my part of the greater LOD cloud/community.

Implementation: A website ties itself to the LOD or semantic web world by including <link/> elements to RDF URIs for the primary topic of a page. This URI can be plugged into a web browser to retrieve RDF about that resource: it’s self-describing. I showed excerpts from a URI for a bill in congress that I created. It has basic metadata, but goes beyond metadata. The pages are auto-generated from a SPARQL DESCRIBE query as I explained in my Census case study on my site

SPARQL: The query language, the SQL, for the semantic web. It is similar to SQL in metaphors and keywords like SELECT, FROM, and WHERE. It differs in every other way. Interestingly, there is a cultural difference: SPARQL servers (“endpoints”) are often made publicly acessible directly, whereas SQL servers are usually private. This might be because SPARQL is read-only.

Example 1: Did a state’s median income predict the votes of Senators on H.R. 1424, the October 2008 stimulus bill? I show the partial RDF graph related to this question and how the graph relates to the SPARQL query. First it is an example SPARQL query. Then the real one. The real one is complicated not because RDF or SPARQL are complicated, but because the data model *I* chose to represent the information is complicated. That is, my data set is very detailed and precise, and it takes a precise query to access it properly. I showed how this data might be plugged into Many Eyes to visualize it.

My visualization dream: Visualization tools like Swivel (ehm: I had real problems getting it to work), Many Eyes, Ggobi, and mapping tools should go from SPARQL query to visualization in one step.

Example 2: Show me the campaign contributions to Rep. Steve Israel (NY-2) by zipcode on a map. I showed the actual SPARQL query I issue on my SPARQL server and a map that I want to generate. In fact, I made a prototype of a form where I can submit any arbitrary SPARQL query and it creates an interactive map showing the information.

Other notes: My SPARQL server uses my own .NET/C# RDF library. That creates a “triple store”, the equivalent of a RDBMS for the semantic web. Underlyingly, though, it stores the triples in a MySQL database with a table whose columns are “subject, predicate, object”, i.e. a table of triples. See also: D2R server for getting existing data online.