The Sunlight Foundation is thrilled to announce the Technology For Integrity program, described below, in partnership with our colleagues from Transparency International Russia. Stronger ties between US and Russian anti-corruption work, from investigation to advocacy to analysis, will help strengthen efforts in both countries. Corruption functions internationally, and using data to help build more transparent, accountable governments should as well. Please share the program description with anyone you think might benefit from such an exchange.

What is Technology for Integrity? 

When you learn to make the world a better place. 

Transparency International Russia (TI-R) and the Sunlight Foundation are thrilled to announce a bilateral educational program that will provide exchange and education, to strengthen ties between key actors in our respective countries in the fight against public corruption while ultimately promoting transparency and the integrity of Russian and American public institutions. 

Big data increasingly affects power. Disclosure of this data has become an important tool that empowers civil society actors to find and use information that was previously hidden or didn’t even exist. 

In Russia, training programs have not kept up with the demands of data science professions. In the United States, there is far more extensive use of data collection, analysis, and visualization in journalism and government accountability work. However, partnerships between Russia and the U.S. in areas involving public sector, big data research are relatively rare. We believe cross-country projects have enormous potential for facilitating the exchange of data and the use of the latest analytical tools while fostering international cooperation in defending the integrity of, and promoting transparency in government. 

We are looking for American and Russian journalists, NGO workers, and civic activists, and students who would like to help us pursue these goals. Participants must have a basic knowledge of data use and want to be involved in helping fight corruption in Russia and the United States. We are waiting for you. If you want to advance your fields of data use for accountability to participate in an exchange where you can also compete to advance your ideas. To apply — please fill in the application form: https://forms.gle/A2UghB4mtmU9FWcb7. 

Please note that we expect participants to have at least a basic knowledge of obtaining data from open data sources. 

Selected participants will participate in a series of webinars that explain the foundations of data-use for anti-corruption. This includes anti-corruption concepts, laws, and processes and the basics in technology use for investigations, analysis and visualization. Topics will include:

• Civic control over budgeting in Russia and the U.S.
• Collecting information on public officials from open sources
• Asset and income declarations 
• An exploration of other successful projects based on official datasets. 

Special attention will be paid to introducing computing languages such as R and Python, basic techniques for cleaning and linking big datasets, and other technical skills.

In addition to the webinars, participants will be asked to pitch project ideas to the larger group. These ideas can focus on work participants are already working on or be brand new. The best pitches will be selected for an in-person meeting in Moscow.

Russian and American participants will then take part in a trip to Moscow to strengthen ties, learn from each other, and collaborate as teams on selected project ideas. The ultimate winning team will also receive a monetary prize. 

Applicants should follow the following link to apply to the program. For questions, please contact Gulnaz Sabirova (edu@transparency.org.ru.), or the Sunlight Foundation at usrussia@sunlightfoundation.com. 

This program has been organized with the support of the Eurasia Foundation, a U.S. based international development organization promoting good governance, civil society development and public administration reform.